j$ddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z ddl mZmZddlmZd d lmZmZmZd d lmZd d lmZmZd d lmZmZmZmZm Z ddl!m"Z"m#Z#gdZ$GddeZ%GddeZ&Gdde Z'deede(deede)de)dede'fdZ*Gdd eZ+ed!"ed#d$f%dd&d'd(deee+efde)de)dede'f d)Z,dS)*)partial)AnyListOptionalUnionN)nnTensor) DeQuantStub QuantStub)Conv2dNormActivationSqueezeExcitation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)_mobilenet_v3_confInvertedResidualInvertedResidualConfigMobileNet_V3_Large_Weights MobileNetV3) _fuse_modules _replace_relu)QuantizableMobileNetV3#MobileNet_V3_Large_QuantizedWeightsmobilenet_v3_largecheZdZdZdededdffd ZdedefdZd d ee ddfd Z fd Z xZ S) QuantizableSqueezeExcitationrargskwargsreturnNctj|d<tj|i|tj|_dS)Nscale_activation)r Hardsigmoidsuper__init__ quantizedFloatFunctionalskip_mulselfr$r% __class__s \C:\Projects\Historical\venv\Lib\site-packages\torchvision/models/quantization/mobilenetv3.pyr+z%QuantizableSqueezeExcitation.__init__!sE%'^!"$)&))) 4466 inputc^|j|||SN)r.mul_scale)r0r4s r2forwardz$QuantizableSqueezeExcitation.forward&s&}  U!3!3U;;;r3is_qatc0t|ddg|ddS)Nfc1 activationTinplace)r)r0r:s r2 fuse_modelz'QuantizableSqueezeExcitation.fuse_model)s$dUL164HHHHHHr3c (|dd}t|dr||dkrtjdgtjdgtjdgtjtjdgtjtjdgtjdgd} | D]\} } || z} | |vr| || <t |||||||dS) Nversionqconfigrg?r)dtyper)z.scale_activation.activation_post_process.scalezFscale_activation.activation_post_process.activation_post_process.scalez3scale_activation.activation_post_process.zero_pointzKscale_activation.activation_post_process.activation_post_process.zero_pointz;scale_activation.activation_post_process.fake_quant_enabledz9scale_activation.activation_post_process.observer_enabled)gethasattrtorchtensorint32itemsr*_load_from_state_dict)r0 state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsrBdefault_state_dictkvfull_keyr1s r2rKz2QuantizableSqueezeExcitation._load_from_state_dict,sC!$$Y55 4 # # -GaKKBG,PSuBUBUZ_ZfhkglZmZmGL|UVTW_d_jGkGkGk_d_kCu{```PU|]^\_O`O`MR\[\Z]M^M^ " " +0022 - -1!A::--+,Jx( %%            r3r6) __name__ __module__ __qualname___versionrr+r r9rboolr@rK __classcell__r1s@r2r#r#sH7c7S7T777777 $P)EPPPP 4466 r3xc|jr.|j|||S||Sr6)use_res_connectrbaddblockr0rcs r2r9z#QuantizableInvertedResidual.forwardYs?   !=$$Q 1 66 6::a== r3)rWrXrYrr+r r9r\r]s@r2r_r_Sst7c7S7T777777!!F!!!!!!!!r3r_cZeZdZdededdffd ZdedefdZd deeddfd Z xZ S) rr$r%r&Nctj|i|t|_t |_dS)zq MobileNet V3 main class Args: Inherits args from floating point MobileNetV3 N)r*r+r quantr dequantr/s r2r+zQuantizableMobileNetV3.__init__as; $)&)))[[ "}} r3rcc||}||}||}|Sr6)rk _forward_implrlrhs r2r9zQuantizableMobileNetV3.forwardls8 JJqMM   q ! ! LLOOr3r:cx|D]}t|turaddg}t|dkr6t|dtjur|dt|||dyt|tur| |dS)N01r r2Tr>) modulestyper lenrReLUappendrr#r@)r0r:mmodules_to_fuses r2r@z!QuantizableMobileNetV3.fuse_modelrs % %AAww...#&*q66Q;;4!::#8#8#**3///a&$GGGGGa888 V$$$ % %r3r6) rWrXrYrr+r r9rr[r@r\r]s@r2rr`s %c %S %T % % % % % %F %%$%4%%%%%%%%r3rinverted_residual_setting last_channelweightsprogressquantizer%r&c |Nt|dt|jdd|jvrt|d|jd|dd}t ||fdt i|}t ||re|dtj j ||_ tj j |d|*|||d |r:tj j |d||S) N num_classes categoriesbackendqnnpackrgT)r:r>)r} check_hash)rrumetapoprr_rr@rGao quantizationget_default_qat_qconfigrC prepare_qatload_state_dictget_state_dictconverteval)rzr{r|r}r~r%rmodels r2_mobilenet_v3_modelr}sPfmSl9S5T5TUUU  $ $ !&)W\)5L M M MjjI..G "#>> g44hSW4XXYYY %%eT%::: Lr3cneZdZedeedddeddejdd d d id d dd Z e Z dS)r zUhttps://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth) crop_sizeiS)rrrzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3z ImageNet-1KgK7A@R@gxV@)zacc@1zacc@5g-?gҍ5@z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_sizerrrecipe unquantized_metrics_ops _file_size_docs)url transformsrN) rWrXrYrrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr3r2r r s#G c7.#>>>!. m5C##      0$GGGr3r quantized_mobilenet_v3_large)name pretrainedc^|ddr tjn tjS)Nr~F)rEr rrr)r%s r2rs, ::j% ( (6:PP ' 5r3)r|TF)r|r}r~c |rtnt|}tdi|\}}t |||||fi|S)a MobileNetV3 (Large) model from `Searching for MobileNetV3 `_. .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool): If True, return a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights :members: :noindex: r!)r!)r rverifyrr)r|r}r~r%rzr{s r2r!r!saZ7?^22D^ffgnooG.@.`.`Y_.`.`+| 8,QY[c n ngm n nnr3)- functoolsrtypingrrrrrGrr torch.ao.quantizationr r ops.miscr rtransforms._presetsr_apirrr_metar_utilsrr mobilenetv3rrrrrutilsrr__all__r#r_rintr[rr r!rr3r2rs3------------ 88888888????????6666667777777777((((((CCCCCCCC0///////   2 2 2 2 2 #42 2 2 j ! ! ! ! !"2 ! ! !%%%%%[%%%:!#$:;!!k "! !  !  !!!!!H$$$$$+$$$83444 6 6 ae 'o'o'o e?A[[\ ]'o'o 'o  'o  'o'o'o54'o'o'or3